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For my graduate research, I am creating a neural network that trains to recognize images. I am going much more complex than just taking a grid of RGB values, downsampling, and sending them to the input of the network like many examples do. I actually use over 100 independently trained neural networks that detect features, such as lines, shading patterns, etc. Much more like the human eye, and it works really well so far! The problem is I have quite a bit of training data. I show it over 100 examples of what a car looks like. Then 100 examples of what a person looks like. Then over 100 of what a dog looks like, etc. This is quite a bit of training data! Currently, I am running at about one week to train the network. This is kind of killing my progress, as I need to adjust and retrain.

I am using Neuroph, as the low-level neural network API. I am running a dual-quadcore machine(16 cores with hyperthreading), so this should be fast. My processor percent is at only 5%. Are there any tricks on Neuroph performance? Or Java performance in general? Suggestions? I am a cognitive psych doctoral student, and I am decent as a programmer, but do not know a great deal about performance programming.

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The first problem is the Neuroph. It is deadly slow. Neuroph has well know major architectural and performance issues. Refer the following link for improving the neuroph performance:

https://dzone.com/articles/nb-improving-neuroph-performance

If you wish to know more about Neuroph Neural Network then visit this Neural Network Tutorial.

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